MLOps & AI Ops Interview Questions and Answers

Description

• MLOps and AIOps Overview
  

  Attribute	MLOps	AIOps
  Primary focus	Model lifecycle: build, test, deploy, monitor	IT operations: event correlation, anomaly detection, automation
  Primary users	Data scientists, ML engineers, DevOps	SREs, IT ops, platform engineers
  Core goals	Reproducibility, CI/CD for models, governance	Reduce MTTR, automate incident response, surface insights
  Key components	Data versioning; model training; CI/CD; model registry; monitoring	Log/metric ingestion; feature extraction; ML-driven correlation; runbooks
  Typical data	Labeled datasets, feature stores, model artifacts	Time-series metrics, logs, traces, topology data
  Maturity/tools	Kubeflow, MLflow, TFX, Seldon	Splunk, Moogsoft, Dynatrace, Elastic APM

  1. Definition — MLOps: MLOps (Machine Learning Operations) is the set of practices and tooling that connects data science with software engineering and DevOps to reliably build, deploy, and manage ML models in production.
  2. Definition — AIOps: AIOps (Artificial Intelligence for IT Operations) applies machine learning and big-data techniques to automate and enhance IT operations such as anomaly detection, event correlation, and automated remediation.
  3. High-level difference: AIOps/MLOps focuses on the model lifecycle and reproducible ML pipelines, while AIOps focuses on operational observability and automating IT workflows using ML.
  4. Data management (MLOps): Core features include data versioning, lineage tracking, feature stores, and automated data validation to ensure training/serving parity.
  5. Model lifecycle (MLOps): Advanced MLOps adds automated retraining, canary/blue-green model deployments, model registries, and drift detection to keep models accurate and compliant.
  6. CI/CD for ML: AIOps/MLOps extends CI/CD with pipeline orchestration, reproducible environments (containers), and automated testing for data and models.
  7. Governance and compliance: MLOps implements model explainability, audit trails, access controls, and bias testing as production-grade requirements.
  8. Monitoring (MLOps): Production monitoring covers prediction quality, data drift, feature distribution shifts, latency, and resource usage with alerting and automated rollback triggers.
  9. Data ingestion (AIOps): AIOps platforms ingest diverse telemetry—logs, metrics, traces, events, and topology maps—and normalize them for ML-driven analysis.
  • Core AIOps features: Typical features include anomaly detection, root-cause analysis, event correlation, noise reduction, and automated remediation playbooks to reduce alert fatigue and MTTR.
  • Advanced AIOps capabilities: At scale AIOps adds causal inference, predictive capacity planning, automated change impact analysis, and closed-loop remediation integrated with orchestration tools.
  • Integration patterns: Both disciplines require tight integration with CI/CD, observability stacks, orchestration platforms, and service catalogs to be effective in production.
  • Automation and feedback loops: Mature implementations use closed-loop automation—models trigger actions and telemetry feeds back to retrain or refine rules and models.
  • Scalability concerns: Production AIOps/MLOps must handle large datasets, distributed training, and model serving at scale; AIOps must process high-volume streaming telemetry with low-latency inference.
  • Tooling ecosystem: AIOps/MLOps tooling emphasizes experiment tracking, model registries, and serving frameworks; AIOps tooling emphasizes ingestion pipelines, correlation engines, and runbook automation.
  • Organizational impact: AIOps/MLOps requires cross-functional collaboration between data science, engineering, and compliance; AIOps requires alignment between SRE, platform, and application teams to act on insights.
  • Success metrics: MLOps success is measured by model accuracy in production, deployment frequency, and time-to-recovery for model issues; AIOps success is measured by reduced alert noise, faster incident resolution, and fewer manual interventions.
  • Adoption advice: Start small with reproducible pipelines and telemetry collection, instrument for observability, add automated tests and monitoring, then iterate toward automated retraining (MLOps) or closed-loop remediation (AIOps).